1. Field of the Invention
The present invention relates to an apparatus and method for the detection of particles in a sample, and more particularly, concerns a flow cytometry apparatus and method for the detection and classification of biological particles of interest from a heterogeneous population of biological particles.
2. Description of the Prior Art
Flow cytometry apparatuses rely upon flow of cells or other particles in a liquid flow stream in order to determine one or more characteristics of the cells under investigation. Further, the flow cytometry apparatus is useful for identifying the presence of certain cells or particles of interest, enumerating those cells or particles and, in some instances, providing a sorting capability so as to be able to collect those cells or particles of interest. In a typical flow cytometry apparatus, a fluid sample containing cells is directed through the flow cytometry apparatus in a rapidly moving liquid stream so that each cell passes serially, and substantially one at a time, through a sensing region. Cell volume may be determined by changes in electrical impedance as each cell passes through the sensing region. Similarly, if an incident beam of light is directed at the sensing region, the passing cells scatter such light as they pass therethrough. This scattered light serves as a function of cell shape and size, index of refraction, opacity, roughness and the like. Further, fluorescence emitted by labeled cells, or autofluorescent cels, which have been excited as a result of passing through the excitation energy of the incident light beam is detectable for indentification of cells having fluorescent properties. After cell analysis is performed by the flow cytometry apparatus, those cells that have been identified as having the desired properties may be sorted if the apparatus has been designed with such capability.
Representative flow cytometry apparatuses are described in U.S. Pat. Nos. 3,826,364 and 4,284,412, and in the publication by Herzenberg et al., "Fluoroescence-activated Cell Sorting," Sci. Am. 234 (3): 108, 1976.
Rapid quantitative analysis of biological cells is proving very useful in biomedical research and clinical medicine. New flow cytometry apparatuses permit quantitative multiparameter analysis of cellular properties at rates of several thousand cells per second. These instrumentts provide the ability to differentiate among cell types by measuring difference between them. Differentiation is based upon the characteristics of fluoroescence, which may be used to detect functional differences of the cells, and light scattering, a function of cell morphology.
Even though advances in flow cytometry techniques have improved cellular analysis, there are still some deficiences in the presently available equipment. For example, flow cytometry techniques may be used to detect the presence of bacteria in human blood, a condition known as bacteremia. Currently accepted diagnostic methods available for detecting bacteremia are based on visual detection of the growth of bacteria in liquid media inoculated with blood samples. Detection of bacteremia by the growth methods is slow, usually requiring two to fourteen days. Flow cytometry techniques using specific labeling of the bacteria with fluorescent dyes and procedures of sample preparation, which allow discrimination of residual blood cells, have been studied. At present, once a positive blood culture has been identified, clinical laboratories utilize additional materials, labor, and time in order to obtain bacterial classification/antibiotic senstivity information. These procedures, although effective, are costly and are virtually all dependent upon bacterial growth. Results are not available until hours later, and in most cases overnight or multiday incubation is required. Accordingly, there is a cleary perceived need to increase the speed of this follow-up testing. Such increase in speed could be achieved by the application of flow cytometry techniques to the non-growth classification of bacteria.
Progress in the application of flow cytometry techniques has also been limited by the lack of sophisticated methods of analysis for handling multi-feature data to provide reliable results. Data obtained by flow cytometry apparatuses are almost always displayed in the form of one-dimensional (histogram) and two-dimensional (contour plot, scatter plot) frequency distributions of measured variables. The partitioning of multiparameter data files involves consecutive use of the interactive one- or two-dimensional graphics programs. This procedure is not only cumbersome, but also loses the advantages of processing data in multi-dimensional space, and may obscure a significant subpopulation of cells.
For example, presently available flow cytometry apparatuses have the capability of measuring four features for each of thousands of cells per second. Data processing for cell classification must transform such large sets of multiparameters input data into meaningful experimental measures, and is an extremely complex task. By far the most popular method of data processing has involved the successive use of rectangular windows in two-dimensions to achieve multi-dimensional analysis.
Quantitative analysis of multiparameter flow cytometric data for rapid cell detection consists of two stages: cell class characterization and sample processing. In general, the process of cell class characterization partitions the cell feature space into disjoint regions of cells of interest and cells not to interest. Then in sample processing, each cell is classified in one of the two categories according to the region into which it falls. Careful analysis of the class of cells under study is very important, because good detection performance may be expected only if an appropriate region for the representation of the cells of interest is obtained. Thus, selecting a specific region for a population of cells is the fundamental operation of cellular detection data analysis. But for multiparameter data, the problem of isolating the region of interest in four-dimensional space by visul inspection is an impractical task. A possible solution for this approach is selecting two two-dimensional windows at a time.
Whether four or five parameter rectangular window gating is utilized, most cell clusters exhibit elliptic or ovoid shape in the coordinates space represented by two color fluorescence detection. The major axes of these clusters are often not parallel to the coordinate axes. The ideal boundary for defining such a region is therefore an ellipsoidal or ovoid figure in multidimension space. The rectangular windows for isolating such clusters usually enclose a great deal of open space thereby assigning unwanted background particles as cells of interest, and consequenty introducing classification error.
There appears to be a need for more sophisticated algorithms to improve the analytical capabilities of the flow cytometry apparatuses. There is no known work which attempts to classify "events" measured by flow cytometry equipment using the techniques of pattern recognition.
Pattern recognition is a field concerned with machine recognition of meaningful regularities in noisy or complex environments. It includes the detection and recognition of invariant properties among sets of measurements describing objects or events. In general, the purpose of pattern recognition is to categorize a sample of observed data as a member of a class. A set of characteristics measurements and relations need to be extracted first for the representation of the class, then classification of the data on the basis of the representation may be performed. This approach has been applied to problems from many diverse fields, including some studies in the field of cytology.
One investigator applied pattern recognition methods to leukocyte images obtained by microscopic examination of specially stained blood smears (Prewitt, J. M. S., "Parametric and Nonparametric Recognition by Computer: An Application to Leukocyte Image Processing," Adv. Computers 12:25-414, 1972). In the Prewitt publication, five tapes of white cells found in normal human peripheral blood were distinguished on the basis of computer processing of digitized microimages. A similar body by Bartels described attempts to statistically characterize cycltologic "profiles" for normal and abnormal cell populations. Several multivariate analysis methods were used to determine the statistically significant differences among the profiles. Cells could then be classified into these two categories with a high degree or accuracy. (Bartels, T. H., "Numerical Evaluation of Cytologic Data, I-VIII, Anal. Quant. Cytol. 1,2,3, (1979, 1980, 1981).
For flow cytometry data processing, little work has been done in applying pattern recognition. In an attempt to match window shapes to cluster outlines in two-dimensional space, one investigator tried to use a quadratic or piecewise-quadratic equation to form a elliptic window (Sharpless, T., "Cytometric Data Processing," in: Flow Cytometry and Sorting, Melamed M. R., Mullaney, P. F., Mendelsohn, M. L. (Eds.) John Wiley and Sons, New York, 1979, pages 359-379). The window may be adjusted to fit the cell cluster. Since the power of discrimination is increased when data are anayzed in high dimensional space, other investigators have described hardware and software for three-dimensional graphical analysis, in which ellipsoidal clusters are separated by planes (Stohr, M., and Futterman, G., "Visualization of Multidimensional Spectra in Flow Cytometry," J. Histochem. Cytochem. 27-560, 1978). However, it is much more difficult to visualize an anaysis space of more than three dimensions, while flow cytometry data typically contains four parameters for each cell.
In the analysis of biological particles, detection of the particles alone is usually not enough. To be clinically useful, some indication of what particles such as bacteria, are present must also be provided so that the clinician may make decisions regarding probable site of infection and suitable antimicrobial therapy. Presently in clinical laboratories, bacterial classification is usually performed by observing or measuring the results of biochemical reactions between specific reagents and bacteria and/or their metabolic products. This process requires a pure bacterial isolate, may take four to six or more hours, and requires inocula of greater than 10.sup.7 colony forming units per milliliter (cfu/ml). As it stands now, the classification information cannot be provided until organisms grow to sufficient numbers to provide inocula for the usual procedure. Therefore, decisions based on organisms actually recovered from the patient are only possible in a two or fourteen day timeframe. Waiting for these results may create a high risk of patient morbidity or mortality. In recent practice, early decisions are usually empirical, based upon patient symptoms. So the need for earlier informed decisions regarding antimicrobial therapy remains. To permit earlier informed decisions, methods are needed which allow classificsation at lower concentrations of biological particles and which may, therefore, be accomplished more quickly.
Thus, the present invention is directed to pattern recognition techniques to improve the performance of particle detection and classification using multiparmeter flow cytometric data.